Lawrence Esswood
Research Associate
Cambridge, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolLawrence Esswood is a Research Associate and PhD in Computer Science based in Cambridge with 22 years of experience focused on operating systems, hypervisor security, and practical adoption of capability-based CHERI technology. He has combined academic research at the University of Cambridge with industry projects—designing an ORAM for data-oblivious computation at Microsoft Research and building network virtualization and large-scale data analysis systems at IMC Trading. A hands-on engineer and collaborator on the CHERI project, he also contributed motion-capture data handling and skeleton visualization work to the well-known SheffieldML/GPy Gaussian processes library. Lawrence excels at translating advanced security architectures into deployable solutions and often works as the technical expert guiding organizations through CHERI adoption. His background blends rigorous formal research with real-world systems engineering, making him well suited to consult on secure platform design and implementation.
22 years of coding experience
1 year of employment as a software developer
Bachelor of Arts - BA, Computer Science, First, Bachelor of Arts - BA, Computer Science, First at University of Cambridge
Github Skills (7)
machine-learning10
python10
data-science10
statistical-models9
data-visualisation9
data-visualization9
data-visualizations9
Programming languages (10)
Github contributions (5)
SheffieldML/GPy
Apr 2013 - May 2021
Gaussian processes framework in python
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:10 reviews, 247 commits, 24 PRs in 8 years 2 months
Contributions summary:Lawrence was primarily focused on adding and modifying motion capture data processing and analysis capabilities to the GPy library. Their commits included implementing functions for loading and handling motion capture data, specifically for CMU and OSU datasets. These changes involved creating and refining classes related to skeleton representation and data visualization within the existing framework, as well as correcting code errors that prevented the code from operating correctly.
gaussiangaussian-processespython
mlresearch/v12
Feb 2017 - Feb 2023
NIPS Mini-Symposium on Causality in Time Series
Contributions:1 review, 77 pushes, 2 branches in 6 years
symposiummachine-learningtime-series-forecastingtime-seriesnips
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